A common requirement in image processing is the enhancement of the image. To perform image enhancement, current systems may first perform feature detection. This may include the identification of any edges in the image. Once various features have been detected, segmentation of the image may be performed. The segmentation may decompose and classify the image into various components, such as text and different types of graphics, e.g., maps, drawings, photos, and other images. This allows for different treatment of different components of the image; enhancement of text may have to be performed differently than the enhancement of a photo, for example. Enhancement may be performed on the different components, according to the type of component. A variety of enhancement algorithms may be adaptively applied, per component. Ideally, this would achieve improved image quality.
Such processing, however, may include inherent inefficiencies. Feature extraction, for example, may include a noise removal process that results in a cleaner image. Such an image may then be used for purposes of calculation of primitives that are needed to define specific features during the feature extraction process. The enhancement stage may also include the creation of a cleaner image. The creation of a cleaner image during the enhancement process is therefore somewhat redundant, in that a cleaner image was previously generated during the noise removal process of feature extraction.
In addition, feature extraction may include a neighborhood analysis and cleanup phase that creates low-level segmentation information. Such information may typically be used in the extraction of features that may then be passed to the segmentation and classification process. The segmentation and classification process may receive these extracted features for purposes of defining specific detected regions. The segmentation and classification process, however, typically generates low-level segmentation information on its own in order to define detected regions. Again, this may represent a redundancy, given that low-level segmentation information was previously produced during the neighborhood analysis and cleanup phase of the feature extraction process.
In the drawings, the leftmost digit(s) of a reference number identifies the drawing in which the reference number first appears.